Remote Sensing of Snow

Transcription

1 Remote Sensing of Snow

2 Remote Sensing Basics A definition: The inference of an area s or object s physical characteristics by distant detection of the range of electromagnetic radiation it reflects and/or emits Advantages o Potentially comprehensive and affordable coverage over wide areas o Independent from surface constraints (eg deep snow, dangerous terrain, inaccessibility) o Based on objective measurements o Repeatable: able to generate spatio-temporal datasets o Provides options to obtain observations in sparsely-instrumented areas Disadvantages o Requirement for extensive processing of large datasets o Technological constraints / overheads of platforms and sensors o Many variables not directly measurable: difficult to validate

3 Remote Sensing Platforms (Stages) Usually assumed to be from satellite-borne sensors full-res/pp1386a3-fig06.jpg view.php?id=80291 But also...

4 Remote Sensing Platforms Aircraft NASA ER-2 Fairbanks FODAR FactSheets/FS-046-DFRC.html

5 Remote Sensing Platforms Dirigibles

6 2014/08/GT_15Aug2014_Fig4.png Remote Sensing Platforms UAVs / Drones /01/24/a_antarctica_composite_rov_image_2.jpg

7 Remote Sensing Platforms Terrestrial Instrumentation ws4/presentations/4e_hayashi.pdf

8 Image Data Images stored as raster (gridded) datasets

9 Satellite Remote Sensing Principal factors affecting RS capabilities Platform... o Orbital altitude: controls speed, and thus orbital period (also affects swath + spatial resolution, for given sensor capability) Sensor... o Swath: lateral spatial coverage from single pass o Temporal resolution: re-visit interval: depends on swath and platform orbital velocity o Spatial resolution: size of smallest detectable object: depends on sensor and altitude o Spectral resolution: number, width, position, sensitivity of wavelength bands o Radiometric resolution: number of sensor sensitivity levels for given band: eg, 8-bit 256 levels Orbital Speed = G. M G Gravitational constant x M Mass of Earth x kg r Orbit radius ie, altitude km r

10 Satellite Remote Sensing Satellite Orbits Geosynchronous Orbit km Orbital period ~ 1 sidereal day Low Earth Orbit < 2000 km eg ISS 340 km Hubble 595 km Most EO platforms Medium Earth Orbit 2000 km km eg GPS constellation km High Earth Orbit > km Mostly communications, but some wide-area observation platforms

11 Satellite Remote Sensing Operational Land Imager (OLI) on LandSat 8 Moderate Resolution Imaging Spectro-Radiometer (MODIS) on Terra, Aqua Polar, sun-synchronous orbits: altitude 705 km: time per orbit ~99 minutes High spatial resolution (1x15m, 8x30m, 2x100m) Narrow swath (185km) Low temporal resolution (16-day re-visit interval) hlc5frq11y_02_04_2013/large/landsat8.jpg Daily re-visit interval (at different look-angles) Wide swath (2330km) Lower spatial resolution (2x250m, 5x500m, 29x1000m)

12 Satellite Remote Sensing Terra Orbit Track 4 Feb

13 Satellite Remote Sensing MODIS 21 April 2013 R = 6 (SWIR) G = 2 (NIR) B = 4 (Red)

14 Satellite Remote Sensing MODIS 21 April 2013 R = 6 (SWIR) G = 2 (NIR) B = 4 (Red) Daily pass J 500m spatial resolution L Daily passes have different look-angles LJ

15 Satellite Remote Sensing LandSat8 OLI 21 April 2013 R = 6 (SWIR) G = 5 (NIR) B = 4 (Red) 16-day re-visit interval L 30m spatial resolution J

16 Satellite Remote Sensing LandSat8 OLI 21 April 2013

17 Satellite Remote Sensing Identifying lake ice-off date Coles Lake area, NE BC 15 May 2014 LandSat8 OLI R = 4 (Red) G = 2 (NIR) B = 6 (SWIR)

18 Some obstacles to making sense of RS imagery General o Atmospheric... o absorption (occurs within specific wavelengths) o scattering (clouds, aerosols, particles) o refraction (at boundaries between atmospheric layers) o What does the reflectance of each pixel depict? o Geolocational uncertainties (what is the footprint of a pixel?)

19 Some obstacles to making sense of RS imagery Snow-Specific o Obscuration o Often cloudy (particularly over mountains) in winter o By vegetation (snow on ground, but not on canopy) o May be difficult to discern snow from cloud o Snow is a collection of scattering grains: radiation of different wavelengths reflects from some grains, passes through others o Snow surface texture is variable, and affects reflection patterns o Snow reflectance depends heavily on relative angles of illumination and viewing o Snowpack metamorphic processes alter reflective properties by (eg) changing grain-size, adding meltwater o Surface reflectance also affected by impurities (dust, soot, pollen, needles, algae)

20 Angular Variation of Snow Reflectance Bi-directional Reflectance Distribution Function (BRDF) Reflectance varies with relative angles of illumination and viewing Both Direct-Beam and Diffuse radiation play important roles Need to know and account for relative positions of Sun + sensor Directional Reflected Diffuse Incident Directional Incident Diffuse Reflected Snow Surface

21 The Electro-Magnetic Spectrum Spectral Irradiance (W/m²/nm) UV Visible Near IR Short-Wave IR The Solar (Short-Wave) Radiation Spectrum λ (nm) Gamma X- Rays U V Infra- Red Micro wave Radio Most useful wavelength ranges for cryospheric RS purposes: From (nm) To (nm) (Gamma) 0.01 Visible Near IR (NIR) Microwave

22 Snow & Ice Spectral Reflectance Profiles Reflectance profile of snow contrasts with that of other surface-covers

23 Snow & Ice Spectral Reflectance Profiles Also possible to distinguish different types of snow / ice by their contrasting reflectance profiles

24 Snow Reflectance Profiles Reflectance sensitivities vary with wavelength Visible Near Infra-Red Short-Wave Infra-Red Blue to Green: o Insensitive to Grain-Size o Sensitive to impurities Red to SWIR o Sensitive to Grain-Size o (Largely) Insensitive to Impurities Dozier J. (2013): Remote sensing of snow in visible and near-infrared wavelengths NASA Snow Remote Sensing Workshop Boulder, Aug. 2013

25 MODIS Spectral Resolution MODIS high-res. bands provide useful range of information Visible Near Infra-Red Short-Wave Infra-Red MODIS Bands 500m / 250m Dozier J. (2013): Remote sensing of snow in visible and near-infrared wavelengths NASA Snow Remote Sensing Workshop Boulder, Aug. 2013

26 Applications of RS to Snow Studies What snow-related information is RS able to provide? Snow-covered area (extent) o Binary (pixel is snow or no-snow ) o Fractional (pixel %age snow-cover) o Sub-pixel (MODIS Snow Cover and Grain-size, MODSCAG) Snow depth Snow-water equivalent Melt onset Albedo

27 Snow Extent Normalised Difference Snow Index (NDSI) NDSI = (ρ vis ρ SWIR ) (ρ vis + ρ SWIR ) ρ vis ρ SWIR visible (usually green) reflectance SWIR reflectance Helps to distinguish between clouds and snow Ratio approach diminishes influence of o atmospheric effects o variations in illumination vs viewing geometry (Because these bands should have similar relative magnitudes under differing conditions)

28 Snow Extent NDSI LandSat8 OLI 21 April 2013

29 Snow Extent MODIS Binary Snow-Cover Identifies pixels as snow-covered (ie, > 50% snow) when o NDSI > 0.4 o and ρ NIR1 > 0.11 o and ρ GREEN > 0.10 o and sfc. temperature <= 280K (+7 C) OR o NDSI < 0.4 and NDVI * > 0.1 Tends to... o Miss low-fraction snow cover (early + late in season) o Miss forest snow when canopy is snow-free o Over-estimate snow-cover in higher elevations * NDVI: Normalised Diff. Vegetation Index Available from Terra- and Aqua-borne sensors as o 500 m: daily and 8-day o 0.05 Climate Modelling Grid: daily, 8-day and monthly

30 Snow Extent MODIS Fractional Snow-Cover Based on empirically-established linear relation between... o MODIS NDSI o pixel fractional snow-cover derived from LandSat ETM+ imagery Made available with binary snow-cover dataset Tends to... o over-estimate through winter o over-estimate in forested terrain o under-estimate in early winter, spring Salomonson V.V. and Appel I. (2004) Estimating fractional snow cover from MODIS using the normalized difference snow index Remote Sensing of Environment 89: pp

31 Snow Extent MODIS Snow-Cover and Grain-Size (MODSCAG) Estimates fractional cover in each MODIS pixel of end-members : o snow (and - importantly - its grain-size) o vegetation o rock / soil o shade Matches reflectance profile across the 7 250m / 500m MODIS bands with the best-fitting analogue from a library of lab.-derived profiles (built by combining different fractions of end-members) Improves on errors of commission / omission found in MODIS datasets Less sensitive to o vegetation type / fractional cover o snow grain size o land surface temperature o heterogeneity of snow or vegetation cover: o where there is substantial snow heterogeneity, o finds too much snow in shrublands o misses snow in barren lands

32 Snow Extent MODIS Snow-Cover and Grain-Size (MODSCAG) Positive values imply overestimates of fractional snow-cover Rittger K., Painter T. H. and Dozier J. (2013) Assessment of methods for mapping snow cover from MODIS Advances in Water Resources 51: pp

33 Snow Depth Problem: How to infer 3 rd dimension? Variation of radar back-scatter with snow depth (limited use so far) Lidar: Light Radar - uses stream of Laser pulses to build DEMs Snow depth from Structure-From-Motion using digital photography

34 Snow Depth Lidar Survey o Travel-time from emitter target detector measured for every pulse o Variety of platforms ( stages ): o usually airborne o some experimentation from satellites o increasing use of terrestrial systems Lidar Scanner On-Board GPS + IRS Provide Location Details GPS Ground Stations Improve Accuracy esa-projects/142-alpsar

35 Snow Depth Lidar point cloud

36 Snow Depth Lidar: Multiple returns see through canopy Processing enables extraction of ground-surface Digital Elevation Model

37 national/photos-and-images/jemez-catalina/ecohydrology/ehp_fig3_899_445_80auto.jpg Snow Depth Multiple passes enable inference of snow depth (by subtraction from snow-free DEM) Highly dependent on precision of location / attitude instrumentation

38 Snow Depth Structure-From-Motion (SFM) Series of digital photographs taken from known (x,y,z) locations Software infers digital point cloud, builds 3D model

39 Snow Depth Structure-From-Motion (SFM)

40 Snow Depth Structure-From-Motion (SFM)

41 Snow Depth Inferring Snow Depth using Structure-From-Motion (SFM) Summer (13 June 2014) Inferred (additional) winter snow depth Winter (20 April 2014) Nolan M., Larsen C.F. and Sturm M. (2015) Mapping snow-depth from manned-aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry The Cryosphere Discussions 9: pp

42 SWE Options for inferring SWE from RS data Multiple efforts to improve capabilities: progress being made Microwave radiation (wavelengths ~1mm to 1m) is sensitive to water content, and is used to estimate SWE (and sometimes snow depth) Two principal techniques: o Passive Microwave o Active Microwave

43 SWE Passive Microwave Basic principles: o microwave radiation is emitted naturally from Earth surface o this radiation is scattered by water in snowpack Longer wavelengths equate to much lower energy than visible, IR: therefore wide-area, relatively coarse spatial resolution (~25 km) Water in snowpack scatters microwaves: SWE inferred from variations in ratio of brightness temps at two wavelengths (1.5cm, 0.8 cm) using empirically-derived equation (currently linear) BUT also affected by grain-size, depth, snowpack stratigraphy, meltwater fraction, ponds / lakes within field of view Principal benefit: these wavelengths not obscured by cloud Of greatest use over dry, shallow snowpacks: used operationally over prairies and tundra since 1978 Much more challenging to apply in areas with wetter and/or deeper snow, or in those with significant amounts of above-snow vegetation (veg. attenuates emissions from surface, but adds its own) But - sensitivity to water makes this useful for identifying melt onset

44 SWE Passive Microwave SWE estimate, 5 Feb

45 SWE Active Microwave (Radar) Basic principles: o microwave radiation emitted by satellite / airborne instrument o in dry snow, microwave radiation penetrates easily o less penetration as water content increases Scattering occurs at o air / snow surface o within snowpack o at snowbase / ground interface o from ground surface Two microwave bands used to make sense of this o Ku (1.7 cm): sensitive to surface scattering o X (3.1 cm): sensitive to volume scattering Higher energy of active system improves spatial resolution c/f passive But again, problems when water and/or vegetation are present

46 Albedo Which Albedo do we want? Albedo: the ratio of reflected to incident radiation Important indicator of energy dynamics But... o What combination of incident / reflected directional and/or diffuse? o What relative angle between illumination and viewing? o What wavelength(s)? o Narrowband ( spectral albedo )? o Broadband?

47 Albedo Which Albedo do we want? Black-Sky Albedo White-Sky Albedo Note: Cases 5, 6, 8, 9 are measurable Cases 1-4 and 7 are conceptual ( directional => infinitessimally small) Schaepman-Strub G., Schaepman M.E., Painter T.H., Dangel S. and Martonchik J.V. (2006) Reflectance quantities in optical remote sensing - definitions and case studies Remote Sensing of Environment 103: pp : DOI: /j.rse

48 Albedo Albedo sources MODIS albedo: based on 16-day BRDF o 16-day path repeat interval o provides 16 different illumination / viewing angles o used to approximate BRDF, and thus provide BSA, WSA for o 7 MODIS 250m / 500m bands o visible light o NIR / SWIR o full solar spectrum Computed from snow grain-size provided by MODSCAG o uses exponential function using coefficients dependent on illumination angle to transform grain-size to broadband albedo Multi-angle Imaging Spectro-Radiometer (MISR) o 9 cameras: 4 forward-looking, 4 rearward-looking (max ) o obtains multiple near-instantaneous (7 mins. from first to last) views o the variety of reflectances observed provides slice through BRDF LandSat sensor (TM / ETM+ / OLI) algorithm (Liang 2000, Smith 2010) o weighted sum / scaling of reflectances in 5 bands

49 MISR: Multi-angle Imaging Spectro-Radiometer

50 Summary Wide range of sensors and platforms used in RS of snow Need to have a firm understanding of what different data products represent, and the information they are able (and not able) to provide Important to select consistent datasets appropriate to a study s spatial and temporal scales, and likely internal frequencies of variation If you see this in your future - build your GIS, RS and coding skillsets!